AI Agent

Knowledge Graphs Help Build Scalable AI Agents

Harness the power of knowledge graphs to manage the metadata driving your AI architecture.

Why AI Agents Need a Semantic Foundation

Most AI agents today are just proof-of-concepts—they demo well but fall apart when deployed at scale. To build an AI agent that lasts, you need a semantic foundation. A knowledge graph helps manage the metadata driving your AI architecture, allowing you to:

01

Improve reliability

Get more accurate results

02

Enhance governable

Govern the data flowing to your AI pipelines

03

Enable scalability

Ensure the tools you build don’t become obsolete within a year

How to Build Scalable AI Agents (the Right Way)

With a solid foundation in place, you can scale AI agents by adding data, enabling new use cases, and transforming enterprise knowledge into functional, language-driven agents.

Step 1
Step 2
Step 3
Step 4
Step 5
Step 1 - Build your business case
Step 1

Build your business case

Clearly define the problem statement, identify your users, and ensure you have access to the necessary data.

Step 2 - Identify and Scope Data
Step 2

Identify and Scope Data

Select the key datasets needed to achieve your goal.

Step 3 - Make Your Data AI-Ready
Step 3

Make Your Data AI-Ready

AI agents require consistent terminology across datasets to be effectively queried.

Step 4 - Orchestrate and Test
Step 4

Orchestrate and Test

With your data ready, start with one AI agent and iteratively refine to boost accuracy and adaptability.

Step 5 - Expand
Step 5

Expand

With a solid foundation in place, you can scale AI agents by adding data, enabling new use cases, and transforming enterprise knowledge into functional, language-driven agents.

TopQuadrant’s Role in Your AI Journey

TopQuadrant makes data AI-ready, governs it with knowledge graphs, and supports scalable AI agents—ensuring accurate, context-aware results that evolve with your enterprise.

Related Knowledge

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